library(coda)
library(bayesplot)
library(ggplot2)
library(ggsci)
library(khroma)
library(tidyverse)
library(reshape2)
library(yardstick)
library(here)
knitr::opts_chunk$set(echo = TRUE, dpi = 300 )
Set up MCSim file
# this markdown file must be saved in top level directory for the following to work; the mcsim code depends on getwd results.
mdir <- "MCSim"
source(here::here(mdir,"setup_MCSim.R"))
# Make mod.exe (used to create mcsim executable from model file)
makemod()
The mod.exe had been created.
id_lut <- multicheck$df_check %>% select(Level) %>% unique () %>%
mutate(dataset = c(
rep("Decatur M Train", 9),
rep("Decatur F Train", 9),
rep("Decatur M Test", 9),
rep("Decatur F Test", 10),
rep("Minnesota Train", 49),
rep("Minnesota Test", 49),
'Paulsboro-Train','Horsham-Train',
'Warminster-Test','Warrington-Train'),
Sex = c(
rep("M", 9),
rep("F", 9),
rep("M", 9),
rep("F", 10),
rep("Mixed", 49),
rep("Mixed", 49),
rep("Mixed", 4)),
City = c(
rep("Decatur", 18),
rep("Decatur", 19),
rep("Minnesota", 98),
'Paulsboro','Horsham','Warminster','Warrington'),
Train_Test = c(
rep("Train", 9),
rep("Train", 9),
rep("Test", 9),
rep("Test", 10),
rep("Train", 49),
rep("Test", 49),
'Train','Train',
'Test','Test'),
datatype = c(
rep("Individual",9+9+9+10+49+49),
rep("Summary",4)),
Simulation = row_number(),
variable = paste0(dataset, " ",Simulation))
id_lut$dataset <- factor(id_lut$dataset,levels=
c("Decatur M Train","Decatur F Train","Arnsberg M Train",
"Arnsberg F Train","Decatur M Test","Decatur F Test","Arnsberg M Test",
"Arnsberg F Test","Minnesota Train","Minnesota Test",
'Lubeck-Bartell-Train', 'Lubeck-Bartell-Test',
'Little Hocking-Bartell-Train', 'Little Hocking-Bartell-Test',
'Little Hocking-Emmett-Test','Paulsboro-Train','Horsham-Train',
'Warminster-Test','Warrington-Train'))
id_lut$City <- factor(id_lut$City,levels =
c("Decatur","Arnsberg","Minnesota",'Lubeck-Bartell',
'Little Hocking-Bartell','Little Hocking-Emmett',
'Paulsboro','Horsham','Warminster','Warrington'))
indiv_lut <- id_lut %>%
filter(City %in% c("Decatur", "Minnesota")) %>%
mutate( dataset = as.factor(dataset))
nv <- data.frame(dataset =unique(indiv_lut$dataset),
variable= rep("Pop GM", 6),
type= rep("Pop GM", 6), stringsAsFactors = FALSE)
set.seed(314159)
indiv_parms <- indiv_lut
lnkparmnames <- paste("ln_k.",gsub("_",".",indiv_parms$Level),".",sep="")
lnVdparmnames <- paste("ln_Vd.",gsub("_",".",indiv_parms$Level),".",sep="")
parmsamp <- apply(multicheck$parms.samp,2,sample,1)
## Random z-score estimate of each parameter
indiv_parms$ln_k.z.samp <- parmsamp[lnkparmnames]
indiv_parms$ln_Vd.z.samp <- parmsamp[lnVdparmnames]
normd <- data.frame(x=qnorm(ppoints(200)))
normd$y <- dnorm(normd$x)
iplotk<-
ggplot(subset(indiv_parms,Train_Test=="Train"))+
geom_histogram(aes(x=ln_k.z.samp,after_stat(density)),bins=20)+facet_wrap(~City,ncol=1)+
geom_line(aes(x=x,y=y),data=normd)+
xlab("Individual z-scores for k") + theme_bw()
iplotVd<-
ggplot(subset(indiv_parms,Train_Test=="Train"))+
geom_histogram(aes(x=ln_Vd.z.samp,after_stat(density)),bins=20)+facet_wrap(~City,ncol=1)+
geom_line(aes(x=x,y=y),data=normd)+
xlab("Individual z-scores for Vd") + theme_bw()
print(iplotk)
print(iplotVd)
ggsave(file.path("output-plots",
paste0( sa,"Indiv_zscores_k_PFOS.pdf")),iplotk,dpi=600)
Saving 3.5 x 3.5 in image
ggsave(file.path("output-plots",
paste0( sa,"Indiv_zscores_Vd_PFOS.pdf")),iplotVd,dpi=600)
Saving 3.5 x 3.5 in image
ggsave(file.path("output-plots",
paste0( sa,"Indiv_zscores_k_PFOS.png")),iplotk,dpi=600)
Saving 3.5 x 3.5 in image
ggsave(file.path("output-plots",
paste0( sa,"Indiv_zscores_Vd_PFOS.png")),iplotVd,dpi=600)
Saving 3.5 x 3.5 in image
This is a Figure 2 panel. Needed to use “scale=1.1” in ggsave to match PFOA.
nrow(multicheck$df_check)
[1] 88000
nrow(id_lut)
[1] 139
multicheck$df_check %>% left_join(id_lut) %>% nrow()
Joining, by = c("Level", "Simulation")
[1] 88000
names(multicheck$df_check)
[1] "Level" "Simulation" "Output_Var" "Time" "Data"
[6] "Prediction"
Level
Simulation
Output_Var
Time
Data
Prediction
multicheck2 <- multicheck$df_check %>% left_join(id_lut)%>%
group_by_at ( vars(-Prediction)) %>%
summarise(Prediction = median(Prediction)) %>%
ungroup() %>%
group_by(City) %>%
mutate(Train_Test = factor(Train_Test, levels = c("Train", "Test")),
`City (datatype)` = factor (paste0("\n", City, "\n(", datatype, ")\n") ),
label = case_when(Train_Test=="Train" ~ "C: PFOS Train",
Train_Test=="Test" ~"D: PFOS Test",
TRUE ~ ""))
Joining, by = c("Level", "Simulation")
`summarise()` has grouped output by 'Level', 'Simulation', 'Output_Var', 'Time', 'Data', 'dataset', 'Sex', 'City', 'Train_Test', 'datatype'. You can override using the `.groups` argument.
#define color for testing boxplots
bp_cols <- c (as.character (khroma::colour("muted")(9)) , "#191919")
bp_cols <-bp_cols[c(1,3, 7, 10:8)]# plot_scheme_colourblind(bp_cols)
### Create aesthetics lookup
aes_lut <- multicheck2 %>% ungroup() %>%
group_by(City, datatype, `City (datatype)` ) %>% summarise () %>% ungroup() %>%
mutate( cols = bp_cols, city_fills = bp_cols ,
# for individual level on point plot (multicheck2), darken outlines for visibility, use standard colors otherwise
city_outlines = if_else(datatype == "Individual" , colorspace::darken(city_fills, 0.3), city_fills) ,
shapes = case_when(datatype == "Individual" & `City` %in% c('Decatur', 'Arnsberg', 'Minnesota') ~ 23,
datatype == "Summary" &`City` %in% c("Horsham", "Warminster", "Warrington") ~ 2,
datatype == "Summary" & `City` == "Paulsboro" ~ 1,
TRUE ~ 18 ),
size = if_else(datatype =="Individual", 1.75, 2.5 ) )
`summarise()` has grouped output by 'City', 'datatype'. You can override using the `.groups` argument.
source( paste0(gsub(basename(here()), 'shared_functions', here()), '/plot_scatter_mcheck.r'))
p2 <- plot_scatter_mcheck(dframe = multicheck2, pfas_nom = pfas_name, aes_lut_fn = aes_lut )
Joining, by = "facet_label"
print(p2)
ggsave(here ("output-plots", paste0( sa,"multicheckplot_", pfas_name,
".pdf")),p2,dpi=600, scale=1.1)
Saving 8.8 x 3.85 in image
ggsave(here ("output-plots", paste0( sa,"multicheckplot_", pfas_name,
".png")),p2,dpi=600, scale=1.1)
Saving 8.8 x 3.85 in image
df_check <- multicheck$df_check
df_check <- subset(df_check,Data > 0)
n1 <- nrow(df_check)
id_chks <- df_check %>% select(Level) %>% unique() %>% bind_cols(id_lut) %>%
mutate(dataset = as.factor(dataset), Sex = as.factor(Sex), City = as.factor(City),
Train_Test = as.factor(Train_Test))
New names:
* Level -> Level...1
* Level -> Level...2
df_check <- df_check %>% left_join(id_chks)%>%
mutate(Dataset = paste(as.character(dataset), Simulation),
Sex = ordered(Sex, levels = c("M", "F", "Mixed"),
labels = c("Female", "Male", "Mixed (all sexes)")))
Joining, by = "Simulation"
n2 <- nrow(df_check)
if(n1 != n2)print("duplicates created in id-lut join")
df_check$Time.desc <- as.character(paste0("T=",df_check$Time))
df_check$Time.desc[df_check$Time.desc == "T=1e-06"] <- "SteadyState"
df_check$Dataset.Time <- interaction(df_check$Dataset,
df_check$Time.desc,lex.order=TRUE)
df_check$Dataset.Time <- factor(df_check$Dataset.Time,
levels=levels(df_check$Dataset.Time))
calibdata <- df_check[,names(df_check) != "Prediction"]
calibdata <- calibdata[!duplicated(calibdata),]
print(calibdata)
Level Simulation Output_Var Time Data Level...1 Level...2
1 1_1_1 1 Cserum_t 0.000000 82.400 1_1_1 1_1_1
2 1_1_1 1 Cserum_t 5.802000 70.300 1_1_1 1_1_1
3 1_1_2 2 Cserum_t 0.000000 32.600 1_1_2 1_1_2
4 1_1_2 2 Cserum_t 5.802000 14.200 1_1_2 1_1_2
5 1_1_3 3 Cserum_t 0.000000 236.000 1_1_3 1_1_3
6 1_1_3 3 Cserum_t 5.802000 75.400 1_1_3 1_1_3
7 1_1_4 4 Cserum_t 0.000000 61.000 1_1_4 1_1_4
8 1_1_4 4 Cserum_t 5.802000 12.800 1_1_4 1_1_4
9 1_1_5 5 Cserum_t 0.000000 182.000 1_1_5 1_1_5
10 1_1_5 5 Cserum_t 5.802000 43.900 1_1_5 1_1_5
11 1_1_6 6 Cserum_t 0.000000 25.300 1_1_6 1_1_6
12 1_1_6 6 Cserum_t 5.802000 18.800 1_1_6 1_1_6
13 1_1_7 7 Cserum_t 0.000000 113.000 1_1_7 1_1_7
14 1_1_7 7 Cserum_t 5.802000 24.000 1_1_7 1_1_7
15 1_1_8 8 Cserum_t 0.000000 78.200 1_1_8 1_1_8
16 1_1_8 8 Cserum_t 5.802000 26.400 1_1_8 1_1_8
17 1_1_9 9 Cserum_t 0.000000 54.400 1_1_9 1_1_9
18 1_1_9 9 Cserum_t 5.802000 26.500 1_1_9 1_1_9
19 1_1_10 10 Cserum_t 0.000000 81.200 1_1_10 1_1_10
20 1_1_10 10 Cserum_t 5.802000 31.500 1_1_10 1_1_10
21 1_1_11 11 Cserum_t 0.000000 70.700 1_1_11 1_1_11
22 1_1_11 11 Cserum_t 5.802000 50.200 1_1_11 1_1_11
23 1_1_12 12 Cserum_t 0.000000 13.700 1_1_12 1_1_12
24 1_1_12 12 Cserum_t 5.802000 12.800 1_1_12 1_1_12
25 1_1_13 13 Cserum_t 0.000000 42.000 1_1_13 1_1_13
26 1_1_13 13 Cserum_t 5.802000 28.100 1_1_13 1_1_13
27 1_1_14 14 Cserum_t 0.000000 98.000 1_1_14 1_1_14
28 1_1_14 14 Cserum_t 5.802000 35.100 1_1_14 1_1_14
29 1_1_15 15 Cserum_t 0.000000 56.900 1_1_15 1_1_15
30 1_1_15 15 Cserum_t 5.802000 45.900 1_1_15 1_1_15
31 1_1_16 16 Cserum_t 0.000000 32.500 1_1_16 1_1_16
32 1_1_16 16 Cserum_t 5.802000 13.300 1_1_16 1_1_16
33 1_1_17 17 Cserum_t 0.000000 60.500 1_1_17 1_1_17
34 1_1_17 17 Cserum_t 5.802000 27.600 1_1_17 1_1_17
35 1_1_18 18 Cserum_t 0.000000 43.800 1_1_18 1_1_18
36 1_1_18 18 Cserum_t 5.802000 34.700 1_1_18 1_1_18
37 1_2_1 19 Cserum_t 0.000000 64.100 1_2_1 1_2_1
38 1_2_1 19 Cserum_t 5.802000 15.000 1_2_1 1_2_1
39 1_2_2 20 Cserum_t 0.000000 89.600 1_2_2 1_2_2
40 1_2_2 20 Cserum_t 5.802000 24.700 1_2_2 1_2_2
41 1_2_3 21 Cserum_t 0.000000 74.700 1_2_3 1_2_3
42 1_2_3 21 Cserum_t 5.802000 39.800 1_2_3 1_2_3
43 1_2_4 22 Cserum_t 0.000000 68.400 1_2_4 1_2_4
44 1_2_4 22 Cserum_t 5.802000 30.000 1_2_4 1_2_4
45 1_2_5 23 Cserum_t 0.000000 72.900 1_2_5 1_2_5
46 1_2_5 23 Cserum_t 5.802000 32.200 1_2_5 1_2_5
47 1_2_6 24 Cserum_t 0.000000 78.100 1_2_6 1_2_6
48 1_2_6 24 Cserum_t 5.802000 45.400 1_2_6 1_2_6
49 1_2_7 25 Cserum_t 0.000000 24.100 1_2_7 1_2_7
50 1_2_7 25 Cserum_t 5.802000 15.400 1_2_7 1_2_7
51 1_2_8 26 Cserum_t 0.000000 60.900 1_2_8 1_2_8
52 1_2_8 26 Cserum_t 5.802000 22.000 1_2_8 1_2_8
53 1_2_9 27 Cserum_t 0.000000 137.000 1_2_9 1_2_9
54 1_2_9 27 Cserum_t 5.802000 70.700 1_2_9 1_2_9
55 1_2_10 28 Cserum_t 0.000000 26.600 1_2_10 1_2_10
56 1_2_10 28 Cserum_t 5.802000 15.200 1_2_10 1_2_10
57 1_2_11 29 Cserum_t 0.000000 120.000 1_2_11 1_2_11
58 1_2_11 29 Cserum_t 5.802000 61.700 1_2_11 1_2_11
59 1_2_12 30 Cserum_t 0.000000 60.900 1_2_12 1_2_12
60 1_2_12 30 Cserum_t 5.802000 22.500 1_2_12 1_2_12
61 1_2_13 31 Cserum_t 0.000000 41.100 1_2_13 1_2_13
62 1_2_13 31 Cserum_t 5.802000 12.400 1_2_13 1_2_13
63 1_2_14 32 Cserum_t 0.000000 39.200 1_2_14 1_2_14
64 1_2_14 32 Cserum_t 5.802000 12.800 1_2_14 1_2_14
65 1_2_15 33 Cserum_t 0.000000 18.100 1_2_15 1_2_15
66 1_2_15 33 Cserum_t 5.802000 13.400 1_2_15 1_2_15
67 1_2_16 34 Cserum_t 0.000000 19.400 1_2_16 1_2_16
68 1_2_16 34 Cserum_t 5.802000 16.800 1_2_16 1_2_16
69 1_2_17 35 Cserum_t 0.000000 21.500 1_2_17 1_2_17
70 1_2_17 35 Cserum_t 5.802000 11.800 1_2_17 1_2_17
71 1_2_18 36 Cserum_t 0.000000 53.800 1_2_18 1_2_18
72 1_2_18 36 Cserum_t 5.802000 30.600 1_2_18 1_2_18
73 1_2_19 37 Cserum_t 0.000000 16.000 1_2_19 1_2_19
74 1_2_19 37 Cserum_t 5.802000 6.700 1_2_19 1_2_19
75 1_3_1 38 Cbgd_Css 0.000001 13.000 1_3_1 1_3_1
76 1_3_2 39 Cbgd_Css 0.000001 50.000 1_3_2 1_3_2
77 1_3_3 40 Cbgd_Css 0.000001 45.000 1_3_3 1_3_3
78 1_3_4 41 Cbgd_Css 0.000001 55.000 1_3_4 1_3_4
79 1_3_5 42 Cbgd_Css 0.000001 58.000 1_3_5 1_3_5
80 1_3_6 43 Cbgd_Css 0.000001 50.000 1_3_6 1_3_6
81 1_3_7 44 Cbgd_Css 0.000001 150.000 1_3_7 1_3_7
82 1_3_8 45 Cbgd_Css 0.000001 12.000 1_3_8 1_3_8
83 1_3_9 46 Cbgd_Css 0.000001 58.000 1_3_9 1_3_9
84 1_3_10 47 Cbgd_Css 0.000001 21.000 1_3_10 1_3_10
85 1_3_11 48 Cbgd_Css 0.000001 19.000 1_3_11 1_3_11
86 1_3_12 49 Cbgd_Css 0.000001 25.000 1_3_12 1_3_12
87 1_3_13 50 Cbgd_Css 0.000001 4.000 1_3_13 1_3_13
88 1_3_14 51 Cbgd_Css 0.000001 32.000 1_3_14 1_3_14
89 1_3_15 52 Cbgd_Css 0.000001 58.000 1_3_15 1_3_15
90 1_3_16 53 Cbgd_Css 0.000001 8.500 1_3_16 1_3_16
91 1_3_17 54 Cbgd_Css 0.000001 5.500 1_3_17 1_3_17
92 1_3_18 55 Cbgd_Css 0.000001 58.000 1_3_18 1_3_18
93 1_3_19 56 Cbgd_Css 0.000001 50.000 1_3_19 1_3_19
94 1_3_20 57 Cbgd_Css 0.000001 145.000 1_3_20 1_3_20
95 1_3_21 58 Cbgd_Css 0.000001 77.000 1_3_21 1_3_21
96 1_3_22 59 Cbgd_Css 0.000001 50.000 1_3_22 1_3_22
97 1_3_23 60 Cbgd_Css 0.000001 90.000 1_3_23 1_3_23
98 1_3_24 61 Cbgd_Css 0.000001 14.000 1_3_24 1_3_24
99 1_3_25 62 Cbgd_Css 0.000001 21.000 1_3_25 1_3_25
100 1_3_26 63 Cbgd_Css 0.000001 35.000 1_3_26 1_3_26
101 1_3_27 64 Cbgd_Css 0.000001 28.000 1_3_27 1_3_27
102 1_3_28 65 Cbgd_Css 0.000001 7.000 1_3_28 1_3_28
103 1_3_29 66 Cbgd_Css 0.000001 150.000 1_3_29 1_3_29
104 1_3_30 67 Cbgd_Css 0.000001 50.000 1_3_30 1_3_30
105 1_3_31 68 Cbgd_Css 0.000001 50.000 1_3_31 1_3_31
106 1_3_32 69 Cbgd_Css 0.000001 70.000 1_3_32 1_3_32
107 1_3_33 70 Cbgd_Css 0.000001 21.000 1_3_33 1_3_33
108 1_3_34 71 Cbgd_Css 0.000001 19.000 1_3_34 1_3_34
109 1_3_35 72 Cbgd_Css 0.000001 40.000 1_3_35 1_3_35
110 1_3_36 73 Cbgd_Css 0.000001 70.000 1_3_36 1_3_36
111 1_3_37 74 Cbgd_Css 0.000001 45.000 1_3_37 1_3_37
112 1_3_38 75 Cbgd_Css 0.000001 22.000 1_3_38 1_3_38
113 1_3_39 76 Cbgd_Css 0.000001 29.000 1_3_39 1_3_39
114 1_3_40 77 Cbgd_Css 0.000001 28.000 1_3_40 1_3_40
115 1_3_41 78 Cbgd_Css 0.000001 6.500 1_3_41 1_3_41
116 1_3_42 79 Cbgd_Css 0.000001 22.000 1_3_42 1_3_42
117 1_3_43 80 Cbgd_Css 0.000001 21.000 1_3_43 1_3_43
118 1_3_44 81 Cbgd_Css 0.000001 41.000 1_3_44 1_3_44
119 1_3_45 82 Cbgd_Css 0.000001 41.000 1_3_45 1_3_45
120 1_3_46 83 Cbgd_Css 0.000001 16.000 1_3_46 1_3_46
121 1_3_47 84 Cbgd_Css 0.000001 70.000 1_3_47 1_3_47
122 1_3_48 85 Cbgd_Css 0.000001 16.000 1_3_48 1_3_48
123 1_3_49 86 Cbgd_Css 0.000001 30.000 1_3_49 1_3_49
124 1_4_1 87 Cbgd_Css 0.000001 3.000 1_4_1 1_4_1
125 1_4_2 88 Cbgd_Css 0.000001 8.700 1_4_2 1_4_2
126 1_4_3 89 Cbgd_Css 0.000001 9.000 1_4_3 1_4_3
127 1_4_4 90 Cbgd_Css 0.000001 11.000 1_4_4 1_4_4
128 1_4_5 91 Cbgd_Css 0.000001 15.000 1_4_5 1_4_5
129 1_4_6 92 Cbgd_Css 0.000001 16.000 1_4_6 1_4_6
130 1_4_7 93 Cbgd_Css 0.000001 40.000 1_4_7 1_4_7
131 1_4_8 94 Cbgd_Css 0.000001 26.000 1_4_8 1_4_8
132 1_4_9 95 Cbgd_Css 0.000001 18.000 1_4_9 1_4_9
133 1_4_10 96 Cbgd_Css 0.000001 20.000 1_4_10 1_4_10
134 1_4_11 97 Cbgd_Css 0.000001 35.000 1_4_11 1_4_11
135 1_4_12 98 Cbgd_Css 0.000001 41.000 1_4_12 1_4_12
136 1_4_13 99 Cbgd_Css 0.000001 12.000 1_4_13 1_4_13
137 1_4_14 100 Cbgd_Css 0.000001 15.000 1_4_14 1_4_14
138 1_4_15 101 Cbgd_Css 0.000001 18.000 1_4_15 1_4_15
139 1_4_16 102 Cbgd_Css 0.000001 20.000 1_4_16 1_4_16
140 1_4_17 103 Cbgd_Css 0.000001 25.000 1_4_17 1_4_17
141 1_4_18 104 Cbgd_Css 0.000001 38.000 1_4_18 1_4_18
142 1_4_19 105 Cbgd_Css 0.000001 160.000 1_4_19 1_4_19
143 1_4_20 106 Cbgd_Css 0.000001 32.000 1_4_20 1_4_20
144 1_4_21 107 Cbgd_Css 0.000001 7.000 1_4_21 1_4_21
145 1_4_22 108 Cbgd_Css 0.000001 28.000 1_4_22 1_4_22
146 1_4_23 109 Cbgd_Css 0.000001 40.000 1_4_23 1_4_23
147 1_4_24 110 Cbgd_Css 0.000001 12.000 1_4_24 1_4_24
148 1_4_25 111 Cbgd_Css 0.000001 80.000 1_4_25 1_4_25
149 1_4_26 112 Cbgd_Css 0.000001 90.000 1_4_26 1_4_26
150 1_4_27 113 Cbgd_Css 0.000001 22.000 1_4_27 1_4_27
151 1_4_28 114 Cbgd_Css 0.000001 50.000 1_4_28 1_4_28
152 1_4_29 115 Cbgd_Css 0.000001 21.000 1_4_29 1_4_29
153 1_4_30 116 Cbgd_Css 0.000001 60.000 1_4_30 1_4_30
154 1_4_31 117 Cbgd_Css 0.000001 61.000 1_4_31 1_4_31
155 1_4_32 118 Cbgd_Css 0.000001 120.000 1_4_32 1_4_32
156 1_4_33 119 Cbgd_Css 0.000001 18.000 1_4_33 1_4_33
157 1_4_34 120 Cbgd_Css 0.000001 35.000 1_4_34 1_4_34
158 1_4_35 121 Cbgd_Css 0.000001 68.000 1_4_35 1_4_35
159 1_4_36 122 Cbgd_Css 0.000001 35.000 1_4_36 1_4_36
160 1_4_37 123 Cbgd_Css 0.000001 53.000 1_4_37 1_4_37
161 1_4_38 124 Cbgd_Css 0.000001 35.000 1_4_38 1_4_38
162 1_4_39 125 Cbgd_Css 0.000001 57.000 1_4_39 1_4_39
163 1_4_40 126 Cbgd_Css 0.000001 58.000 1_4_40 1_4_40
164 1_4_41 127 Cbgd_Css 0.000001 71.000 1_4_41 1_4_41
165 1_4_42 128 Cbgd_Css 0.000001 65.000 1_4_42 1_4_42
166 1_4_43 129 Cbgd_Css 0.000001 18.000 1_4_43 1_4_43
167 1_4_44 130 Cbgd_Css 0.000001 40.000 1_4_44 1_4_44
168 1_4_45 131 Cbgd_Css 0.000001 26.000 1_4_45 1_4_45
169 1_4_46 132 Cbgd_Css 0.000001 90.000 1_4_46 1_4_46
170 1_4_47 133 Cbgd_Css 0.000001 91.000 1_4_47 1_4_47
171 1_4_48 134 Cbgd_Css 0.000001 180.000 1_4_48 1_4_48
172 1_4_49 135 Cbgd_Css 0.000001 130.000 1_4_49 1_4_49
173 1_5_1 136 M_Cbgd_Css 2.200000 7.690 1_5_1 1_5_1
174 1_6_1 137 M_Cbgd_Css 2.000000 24.639 1_6_1 1_6_1
175 1_7_1 138 M_Cbgd_Css 2.000000 21.378 1_7_1 1_7_1
176 1_8_1 139 M_Cbgd_Css 2.000000 20.754 1_8_1 1_8_1
dataset Sex City Train_Test datatype
1 Decatur M Train Female Decatur Train Individual
2 Decatur M Train Female Decatur Train Individual
3 Decatur M Train Female Decatur Train Individual
4 Decatur M Train Female Decatur Train Individual
5 Decatur M Train Female Decatur Train Individual
6 Decatur M Train Female Decatur Train Individual
7 Decatur M Train Female Decatur Train Individual
8 Decatur M Train Female Decatur Train Individual
9 Decatur M Train Female Decatur Train Individual
10 Decatur M Train Female Decatur Train Individual
11 Decatur M Train Female Decatur Train Individual
12 Decatur M Train Female Decatur Train Individual
13 Decatur M Train Female Decatur Train Individual
14 Decatur M Train Female Decatur Train Individual
15 Decatur M Train Female Decatur Train Individual
16 Decatur M Train Female Decatur Train Individual
17 Decatur M Train Female Decatur Train Individual
18 Decatur M Train Female Decatur Train Individual
19 Decatur F Train Male Decatur Train Individual
20 Decatur F Train Male Decatur Train Individual
21 Decatur F Train Male Decatur Train Individual
22 Decatur F Train Male Decatur Train Individual
23 Decatur F Train Male Decatur Train Individual
24 Decatur F Train Male Decatur Train Individual
25 Decatur F Train Male Decatur Train Individual
26 Decatur F Train Male Decatur Train Individual
27 Decatur F Train Male Decatur Train Individual
28 Decatur F Train Male Decatur Train Individual
29 Decatur F Train Male Decatur Train Individual
30 Decatur F Train Male Decatur Train Individual
31 Decatur F Train Male Decatur Train Individual
32 Decatur F Train Male Decatur Train Individual
33 Decatur F Train Male Decatur Train Individual
34 Decatur F Train Male Decatur Train Individual
35 Decatur F Train Male Decatur Train Individual
36 Decatur F Train Male Decatur Train Individual
37 Decatur M Test Female Decatur Test Individual
38 Decatur M Test Female Decatur Test Individual
39 Decatur M Test Female Decatur Test Individual
40 Decatur M Test Female Decatur Test Individual
41 Decatur M Test Female Decatur Test Individual
42 Decatur M Test Female Decatur Test Individual
43 Decatur M Test Female Decatur Test Individual
44 Decatur M Test Female Decatur Test Individual
45 Decatur M Test Female Decatur Test Individual
46 Decatur M Test Female Decatur Test Individual
47 Decatur M Test Female Decatur Test Individual
48 Decatur M Test Female Decatur Test Individual
49 Decatur M Test Female Decatur Test Individual
50 Decatur M Test Female Decatur Test Individual
51 Decatur M Test Female Decatur Test Individual
52 Decatur M Test Female Decatur Test Individual
53 Decatur M Test Female Decatur Test Individual
54 Decatur M Test Female Decatur Test Individual
55 Decatur F Test Male Decatur Test Individual
56 Decatur F Test Male Decatur Test Individual
57 Decatur F Test Male Decatur Test Individual
58 Decatur F Test Male Decatur Test Individual
59 Decatur F Test Male Decatur Test Individual
60 Decatur F Test Male Decatur Test Individual
61 Decatur F Test Male Decatur Test Individual
62 Decatur F Test Male Decatur Test Individual
63 Decatur F Test Male Decatur Test Individual
64 Decatur F Test Male Decatur Test Individual
65 Decatur F Test Male Decatur Test Individual
66 Decatur F Test Male Decatur Test Individual
67 Decatur F Test Male Decatur Test Individual
68 Decatur F Test Male Decatur Test Individual
69 Decatur F Test Male Decatur Test Individual
70 Decatur F Test Male Decatur Test Individual
71 Decatur F Test Male Decatur Test Individual
72 Decatur F Test Male Decatur Test Individual
73 Decatur F Test Male Decatur Test Individual
74 Decatur F Test Male Decatur Test Individual
75 Minnesota Train Mixed (all sexes) Minnesota Train Individual
76 Minnesota Train Mixed (all sexes) Minnesota Train Individual
77 Minnesota Train Mixed (all sexes) Minnesota Train Individual
78 Minnesota Train Mixed (all sexes) Minnesota Train Individual
79 Minnesota Train Mixed (all sexes) Minnesota Train Individual
80 Minnesota Train Mixed (all sexes) Minnesota Train Individual
81 Minnesota Train Mixed (all sexes) Minnesota Train Individual
82 Minnesota Train Mixed (all sexes) Minnesota Train Individual
83 Minnesota Train Mixed (all sexes) Minnesota Train Individual
84 Minnesota Train Mixed (all sexes) Minnesota Train Individual
85 Minnesota Train Mixed (all sexes) Minnesota Train Individual
86 Minnesota Train Mixed (all sexes) Minnesota Train Individual
87 Minnesota Train Mixed (all sexes) Minnesota Train Individual
88 Minnesota Train Mixed (all sexes) Minnesota Train Individual
89 Minnesota Train Mixed (all sexes) Minnesota Train Individual
90 Minnesota Train Mixed (all sexes) Minnesota Train Individual
91 Minnesota Train Mixed (all sexes) Minnesota Train Individual
92 Minnesota Train Mixed (all sexes) Minnesota Train Individual
93 Minnesota Train Mixed (all sexes) Minnesota Train Individual
94 Minnesota Train Mixed (all sexes) Minnesota Train Individual
95 Minnesota Train Mixed (all sexes) Minnesota Train Individual
96 Minnesota Train Mixed (all sexes) Minnesota Train Individual
97 Minnesota Train Mixed (all sexes) Minnesota Train Individual
98 Minnesota Train Mixed (all sexes) Minnesota Train Individual
99 Minnesota Train Mixed (all sexes) Minnesota Train Individual
100 Minnesota Train Mixed (all sexes) Minnesota Train Individual
101 Minnesota Train Mixed (all sexes) Minnesota Train Individual
102 Minnesota Train Mixed (all sexes) Minnesota Train Individual
103 Minnesota Train Mixed (all sexes) Minnesota Train Individual
104 Minnesota Train Mixed (all sexes) Minnesota Train Individual
105 Minnesota Train Mixed (all sexes) Minnesota Train Individual
106 Minnesota Train Mixed (all sexes) Minnesota Train Individual
107 Minnesota Train Mixed (all sexes) Minnesota Train Individual
108 Minnesota Train Mixed (all sexes) Minnesota Train Individual
109 Minnesota Train Mixed (all sexes) Minnesota Train Individual
110 Minnesota Train Mixed (all sexes) Minnesota Train Individual
111 Minnesota Train Mixed (all sexes) Minnesota Train Individual
112 Minnesota Train Mixed (all sexes) Minnesota Train Individual
113 Minnesota Train Mixed (all sexes) Minnesota Train Individual
114 Minnesota Train Mixed (all sexes) Minnesota Train Individual
115 Minnesota Train Mixed (all sexes) Minnesota Train Individual
116 Minnesota Train Mixed (all sexes) Minnesota Train Individual
117 Minnesota Train Mixed (all sexes) Minnesota Train Individual
118 Minnesota Train Mixed (all sexes) Minnesota Train Individual
119 Minnesota Train Mixed (all sexes) Minnesota Train Individual
120 Minnesota Train Mixed (all sexes) Minnesota Train Individual
121 Minnesota Train Mixed (all sexes) Minnesota Train Individual
122 Minnesota Train Mixed (all sexes) Minnesota Train Individual
123 Minnesota Train Mixed (all sexes) Minnesota Train Individual
124 Minnesota Test Mixed (all sexes) Minnesota Test Individual
125 Minnesota Test Mixed (all sexes) Minnesota Test Individual
126 Minnesota Test Mixed (all sexes) Minnesota Test Individual
127 Minnesota Test Mixed (all sexes) Minnesota Test Individual
128 Minnesota Test Mixed (all sexes) Minnesota Test Individual
129 Minnesota Test Mixed (all sexes) Minnesota Test Individual
130 Minnesota Test Mixed (all sexes) Minnesota Test Individual
131 Minnesota Test Mixed (all sexes) Minnesota Test Individual
132 Minnesota Test Mixed (all sexes) Minnesota Test Individual
133 Minnesota Test Mixed (all sexes) Minnesota Test Individual
134 Minnesota Test Mixed (all sexes) Minnesota Test Individual
135 Minnesota Test Mixed (all sexes) Minnesota Test Individual
136 Minnesota Test Mixed (all sexes) Minnesota Test Individual
137 Minnesota Test Mixed (all sexes) Minnesota Test Individual
138 Minnesota Test Mixed (all sexes) Minnesota Test Individual
139 Minnesota Test Mixed (all sexes) Minnesota Test Individual
140 Minnesota Test Mixed (all sexes) Minnesota Test Individual
141 Minnesota Test Mixed (all sexes) Minnesota Test Individual
142 Minnesota Test Mixed (all sexes) Minnesota Test Individual
143 Minnesota Test Mixed (all sexes) Minnesota Test Individual
144 Minnesota Test Mixed (all sexes) Minnesota Test Individual
145 Minnesota Test Mixed (all sexes) Minnesota Test Individual
146 Minnesota Test Mixed (all sexes) Minnesota Test Individual
147 Minnesota Test Mixed (all sexes) Minnesota Test Individual
148 Minnesota Test Mixed (all sexes) Minnesota Test Individual
149 Minnesota Test Mixed (all sexes) Minnesota Test Individual
150 Minnesota Test Mixed (all sexes) Minnesota Test Individual
151 Minnesota Test Mixed (all sexes) Minnesota Test Individual
152 Minnesota Test Mixed (all sexes) Minnesota Test Individual
153 Minnesota Test Mixed (all sexes) Minnesota Test Individual
154 Minnesota Test Mixed (all sexes) Minnesota Test Individual
155 Minnesota Test Mixed (all sexes) Minnesota Test Individual
156 Minnesota Test Mixed (all sexes) Minnesota Test Individual
157 Minnesota Test Mixed (all sexes) Minnesota Test Individual
158 Minnesota Test Mixed (all sexes) Minnesota Test Individual
159 Minnesota Test Mixed (all sexes) Minnesota Test Individual
160 Minnesota Test Mixed (all sexes) Minnesota Test Individual
161 Minnesota Test Mixed (all sexes) Minnesota Test Individual
162 Minnesota Test Mixed (all sexes) Minnesota Test Individual
163 Minnesota Test Mixed (all sexes) Minnesota Test Individual
164 Minnesota Test Mixed (all sexes) Minnesota Test Individual
165 Minnesota Test Mixed (all sexes) Minnesota Test Individual
166 Minnesota Test Mixed (all sexes) Minnesota Test Individual
167 Minnesota Test Mixed (all sexes) Minnesota Test Individual
168 Minnesota Test Mixed (all sexes) Minnesota Test Individual
169 Minnesota Test Mixed (all sexes) Minnesota Test Individual
170 Minnesota Test Mixed (all sexes) Minnesota Test Individual
171 Minnesota Test Mixed (all sexes) Minnesota Test Individual
172 Minnesota Test Mixed (all sexes) Minnesota Test Individual
173 Paulsboro-Train Mixed (all sexes) Paulsboro Train Summary
174 Horsham-Train Mixed (all sexes) Horsham Train Summary
175 Warminster-Test Mixed (all sexes) Warminster Test Summary
176 Warrington-Train Mixed (all sexes) Warrington Test Summary
variable Dataset Time.desc
1 Decatur M Train 1 Decatur M Train 1 T=0
2 Decatur M Train 1 Decatur M Train 1 T=5.802
3 Decatur M Train 2 Decatur M Train 2 T=0
4 Decatur M Train 2 Decatur M Train 2 T=5.802
5 Decatur M Train 3 Decatur M Train 3 T=0
6 Decatur M Train 3 Decatur M Train 3 T=5.802
7 Decatur M Train 4 Decatur M Train 4 T=0
8 Decatur M Train 4 Decatur M Train 4 T=5.802
9 Decatur M Train 5 Decatur M Train 5 T=0
10 Decatur M Train 5 Decatur M Train 5 T=5.802
11 Decatur M Train 6 Decatur M Train 6 T=0
12 Decatur M Train 6 Decatur M Train 6 T=5.802
13 Decatur M Train 7 Decatur M Train 7 T=0
14 Decatur M Train 7 Decatur M Train 7 T=5.802
15 Decatur M Train 8 Decatur M Train 8 T=0
16 Decatur M Train 8 Decatur M Train 8 T=5.802
17 Decatur M Train 9 Decatur M Train 9 T=0
18 Decatur M Train 9 Decatur M Train 9 T=5.802
19 Decatur F Train 10 Decatur F Train 10 T=0
20 Decatur F Train 10 Decatur F Train 10 T=5.802
21 Decatur F Train 11 Decatur F Train 11 T=0
22 Decatur F Train 11 Decatur F Train 11 T=5.802
23 Decatur F Train 12 Decatur F Train 12 T=0
24 Decatur F Train 12 Decatur F Train 12 T=5.802
25 Decatur F Train 13 Decatur F Train 13 T=0
26 Decatur F Train 13 Decatur F Train 13 T=5.802
27 Decatur F Train 14 Decatur F Train 14 T=0
28 Decatur F Train 14 Decatur F Train 14 T=5.802
29 Decatur F Train 15 Decatur F Train 15 T=0
30 Decatur F Train 15 Decatur F Train 15 T=5.802
31 Decatur F Train 16 Decatur F Train 16 T=0
32 Decatur F Train 16 Decatur F Train 16 T=5.802
33 Decatur F Train 17 Decatur F Train 17 T=0
34 Decatur F Train 17 Decatur F Train 17 T=5.802
35 Decatur F Train 18 Decatur F Train 18 T=0
36 Decatur F Train 18 Decatur F Train 18 T=5.802
37 Decatur M Test 19 Decatur M Test 19 T=0
38 Decatur M Test 19 Decatur M Test 19 T=5.802
39 Decatur M Test 20 Decatur M Test 20 T=0
40 Decatur M Test 20 Decatur M Test 20 T=5.802
41 Decatur M Test 21 Decatur M Test 21 T=0
42 Decatur M Test 21 Decatur M Test 21 T=5.802
43 Decatur M Test 22 Decatur M Test 22 T=0
44 Decatur M Test 22 Decatur M Test 22 T=5.802
45 Decatur M Test 23 Decatur M Test 23 T=0
46 Decatur M Test 23 Decatur M Test 23 T=5.802
47 Decatur M Test 24 Decatur M Test 24 T=0
48 Decatur M Test 24 Decatur M Test 24 T=5.802
49 Decatur M Test 25 Decatur M Test 25 T=0
50 Decatur M Test 25 Decatur M Test 25 T=5.802
51 Decatur M Test 26 Decatur M Test 26 T=0
52 Decatur M Test 26 Decatur M Test 26 T=5.802
53 Decatur M Test 27 Decatur M Test 27 T=0
54 Decatur M Test 27 Decatur M Test 27 T=5.802
55 Decatur F Test 28 Decatur F Test 28 T=0
56 Decatur F Test 28 Decatur F Test 28 T=5.802
57 Decatur F Test 29 Decatur F Test 29 T=0
58 Decatur F Test 29 Decatur F Test 29 T=5.802
59 Decatur F Test 30 Decatur F Test 30 T=0
60 Decatur F Test 30 Decatur F Test 30 T=5.802
61 Decatur F Test 31 Decatur F Test 31 T=0
62 Decatur F Test 31 Decatur F Test 31 T=5.802
63 Decatur F Test 32 Decatur F Test 32 T=0
64 Decatur F Test 32 Decatur F Test 32 T=5.802
65 Decatur F Test 33 Decatur F Test 33 T=0
66 Decatur F Test 33 Decatur F Test 33 T=5.802
67 Decatur F Test 34 Decatur F Test 34 T=0
68 Decatur F Test 34 Decatur F Test 34 T=5.802
69 Decatur F Test 35 Decatur F Test 35 T=0
70 Decatur F Test 35 Decatur F Test 35 T=5.802
71 Decatur F Test 36 Decatur F Test 36 T=0
72 Decatur F Test 36 Decatur F Test 36 T=5.802
73 Decatur F Test 37 Decatur F Test 37 T=0
74 Decatur F Test 37 Decatur F Test 37 T=5.802
75 Minnesota Train 38 Minnesota Train 38 SteadyState
76 Minnesota Train 39 Minnesota Train 39 SteadyState
77 Minnesota Train 40 Minnesota Train 40 SteadyState
78 Minnesota Train 41 Minnesota Train 41 SteadyState
79 Minnesota Train 42 Minnesota Train 42 SteadyState
80 Minnesota Train 43 Minnesota Train 43 SteadyState
81 Minnesota Train 44 Minnesota Train 44 SteadyState
82 Minnesota Train 45 Minnesota Train 45 SteadyState
83 Minnesota Train 46 Minnesota Train 46 SteadyState
84 Minnesota Train 47 Minnesota Train 47 SteadyState
85 Minnesota Train 48 Minnesota Train 48 SteadyState
86 Minnesota Train 49 Minnesota Train 49 SteadyState
87 Minnesota Train 50 Minnesota Train 50 SteadyState
88 Minnesota Train 51 Minnesota Train 51 SteadyState
89 Minnesota Train 52 Minnesota Train 52 SteadyState
90 Minnesota Train 53 Minnesota Train 53 SteadyState
91 Minnesota Train 54 Minnesota Train 54 SteadyState
92 Minnesota Train 55 Minnesota Train 55 SteadyState
93 Minnesota Train 56 Minnesota Train 56 SteadyState
94 Minnesota Train 57 Minnesota Train 57 SteadyState
95 Minnesota Train 58 Minnesota Train 58 SteadyState
96 Minnesota Train 59 Minnesota Train 59 SteadyState
97 Minnesota Train 60 Minnesota Train 60 SteadyState
98 Minnesota Train 61 Minnesota Train 61 SteadyState
99 Minnesota Train 62 Minnesota Train 62 SteadyState
100 Minnesota Train 63 Minnesota Train 63 SteadyState
101 Minnesota Train 64 Minnesota Train 64 SteadyState
102 Minnesota Train 65 Minnesota Train 65 SteadyState
103 Minnesota Train 66 Minnesota Train 66 SteadyState
104 Minnesota Train 67 Minnesota Train 67 SteadyState
105 Minnesota Train 68 Minnesota Train 68 SteadyState
106 Minnesota Train 69 Minnesota Train 69 SteadyState
107 Minnesota Train 70 Minnesota Train 70 SteadyState
108 Minnesota Train 71 Minnesota Train 71 SteadyState
109 Minnesota Train 72 Minnesota Train 72 SteadyState
110 Minnesota Train 73 Minnesota Train 73 SteadyState
111 Minnesota Train 74 Minnesota Train 74 SteadyState
112 Minnesota Train 75 Minnesota Train 75 SteadyState
113 Minnesota Train 76 Minnesota Train 76 SteadyState
114 Minnesota Train 77 Minnesota Train 77 SteadyState
115 Minnesota Train 78 Minnesota Train 78 SteadyState
116 Minnesota Train 79 Minnesota Train 79 SteadyState
117 Minnesota Train 80 Minnesota Train 80 SteadyState
118 Minnesota Train 81 Minnesota Train 81 SteadyState
119 Minnesota Train 82 Minnesota Train 82 SteadyState
120 Minnesota Train 83 Minnesota Train 83 SteadyState
121 Minnesota Train 84 Minnesota Train 84 SteadyState
122 Minnesota Train 85 Minnesota Train 85 SteadyState
123 Minnesota Train 86 Minnesota Train 86 SteadyState
124 Minnesota Test 87 Minnesota Test 87 SteadyState
125 Minnesota Test 88 Minnesota Test 88 SteadyState
126 Minnesota Test 89 Minnesota Test 89 SteadyState
127 Minnesota Test 90 Minnesota Test 90 SteadyState
128 Minnesota Test 91 Minnesota Test 91 SteadyState
129 Minnesota Test 92 Minnesota Test 92 SteadyState
130 Minnesota Test 93 Minnesota Test 93 SteadyState
131 Minnesota Test 94 Minnesota Test 94 SteadyState
132 Minnesota Test 95 Minnesota Test 95 SteadyState
133 Minnesota Test 96 Minnesota Test 96 SteadyState
134 Minnesota Test 97 Minnesota Test 97 SteadyState
135 Minnesota Test 98 Minnesota Test 98 SteadyState
136 Minnesota Test 99 Minnesota Test 99 SteadyState
137 Minnesota Test 100 Minnesota Test 100 SteadyState
138 Minnesota Test 101 Minnesota Test 101 SteadyState
139 Minnesota Test 102 Minnesota Test 102 SteadyState
140 Minnesota Test 103 Minnesota Test 103 SteadyState
141 Minnesota Test 104 Minnesota Test 104 SteadyState
142 Minnesota Test 105 Minnesota Test 105 SteadyState
143 Minnesota Test 106 Minnesota Test 106 SteadyState
144 Minnesota Test 107 Minnesota Test 107 SteadyState
145 Minnesota Test 108 Minnesota Test 108 SteadyState
146 Minnesota Test 109 Minnesota Test 109 SteadyState
147 Minnesota Test 110 Minnesota Test 110 SteadyState
148 Minnesota Test 111 Minnesota Test 111 SteadyState
149 Minnesota Test 112 Minnesota Test 112 SteadyState
150 Minnesota Test 113 Minnesota Test 113 SteadyState
151 Minnesota Test 114 Minnesota Test 114 SteadyState
152 Minnesota Test 115 Minnesota Test 115 SteadyState
153 Minnesota Test 116 Minnesota Test 116 SteadyState
154 Minnesota Test 117 Minnesota Test 117 SteadyState
155 Minnesota Test 118 Minnesota Test 118 SteadyState
156 Minnesota Test 119 Minnesota Test 119 SteadyState
157 Minnesota Test 120 Minnesota Test 120 SteadyState
158 Minnesota Test 121 Minnesota Test 121 SteadyState
159 Minnesota Test 122 Minnesota Test 122 SteadyState
160 Minnesota Test 123 Minnesota Test 123 SteadyState
161 Minnesota Test 124 Minnesota Test 124 SteadyState
162 Minnesota Test 125 Minnesota Test 125 SteadyState
163 Minnesota Test 126 Minnesota Test 126 SteadyState
164 Minnesota Test 127 Minnesota Test 127 SteadyState
165 Minnesota Test 128 Minnesota Test 128 SteadyState
166 Minnesota Test 129 Minnesota Test 129 SteadyState
167 Minnesota Test 130 Minnesota Test 130 SteadyState
168 Minnesota Test 131 Minnesota Test 131 SteadyState
169 Minnesota Test 132 Minnesota Test 132 SteadyState
170 Minnesota Test 133 Minnesota Test 133 SteadyState
171 Minnesota Test 134 Minnesota Test 134 SteadyState
172 Minnesota Test 135 Minnesota Test 135 SteadyState
173 Paulsboro-Train 136 Paulsboro-Train 136 T=2.2
174 Horsham-Train 137 Horsham-Train 137 T=2
175 Warminster-Test 138 Warminster-Test 138 T=2
176 Warrington-Train 139 Warrington-Train 139 T=2
Dataset.Time
1 Decatur M Train 1.T=0
2 Decatur M Train 1.T=5.802
3 Decatur M Train 2.T=0
4 Decatur M Train 2.T=5.802
5 Decatur M Train 3.T=0
6 Decatur M Train 3.T=5.802
7 Decatur M Train 4.T=0
8 Decatur M Train 4.T=5.802
9 Decatur M Train 5.T=0
10 Decatur M Train 5.T=5.802
11 Decatur M Train 6.T=0
12 Decatur M Train 6.T=5.802
13 Decatur M Train 7.T=0
14 Decatur M Train 7.T=5.802
15 Decatur M Train 8.T=0
16 Decatur M Train 8.T=5.802
17 Decatur M Train 9.T=0
18 Decatur M Train 9.T=5.802
19 Decatur F Train 10.T=0
20 Decatur F Train 10.T=5.802
21 Decatur F Train 11.T=0
22 Decatur F Train 11.T=5.802
23 Decatur F Train 12.T=0
24 Decatur F Train 12.T=5.802
25 Decatur F Train 13.T=0
26 Decatur F Train 13.T=5.802
27 Decatur F Train 14.T=0
28 Decatur F Train 14.T=5.802
29 Decatur F Train 15.T=0
30 Decatur F Train 15.T=5.802
31 Decatur F Train 16.T=0
32 Decatur F Train 16.T=5.802
33 Decatur F Train 17.T=0
34 Decatur F Train 17.T=5.802
35 Decatur F Train 18.T=0
36 Decatur F Train 18.T=5.802
37 Decatur M Test 19.T=0
38 Decatur M Test 19.T=5.802
39 Decatur M Test 20.T=0
40 Decatur M Test 20.T=5.802
41 Decatur M Test 21.T=0
42 Decatur M Test 21.T=5.802
43 Decatur M Test 22.T=0
44 Decatur M Test 22.T=5.802
45 Decatur M Test 23.T=0
46 Decatur M Test 23.T=5.802
47 Decatur M Test 24.T=0
48 Decatur M Test 24.T=5.802
49 Decatur M Test 25.T=0
50 Decatur M Test 25.T=5.802
51 Decatur M Test 26.T=0
52 Decatur M Test 26.T=5.802
53 Decatur M Test 27.T=0
54 Decatur M Test 27.T=5.802
55 Decatur F Test 28.T=0
56 Decatur F Test 28.T=5.802
57 Decatur F Test 29.T=0
58 Decatur F Test 29.T=5.802
59 Decatur F Test 30.T=0
60 Decatur F Test 30.T=5.802
61 Decatur F Test 31.T=0
62 Decatur F Test 31.T=5.802
63 Decatur F Test 32.T=0
64 Decatur F Test 32.T=5.802
65 Decatur F Test 33.T=0
66 Decatur F Test 33.T=5.802
67 Decatur F Test 34.T=0
68 Decatur F Test 34.T=5.802
69 Decatur F Test 35.T=0
70 Decatur F Test 35.T=5.802
71 Decatur F Test 36.T=0
72 Decatur F Test 36.T=5.802
73 Decatur F Test 37.T=0
74 Decatur F Test 37.T=5.802
75 Minnesota Train 38.SteadyState
76 Minnesota Train 39.SteadyState
77 Minnesota Train 40.SteadyState
78 Minnesota Train 41.SteadyState
79 Minnesota Train 42.SteadyState
80 Minnesota Train 43.SteadyState
81 Minnesota Train 44.SteadyState
82 Minnesota Train 45.SteadyState
83 Minnesota Train 46.SteadyState
84 Minnesota Train 47.SteadyState
85 Minnesota Train 48.SteadyState
86 Minnesota Train 49.SteadyState
87 Minnesota Train 50.SteadyState
88 Minnesota Train 51.SteadyState
89 Minnesota Train 52.SteadyState
90 Minnesota Train 53.SteadyState
91 Minnesota Train 54.SteadyState
92 Minnesota Train 55.SteadyState
93 Minnesota Train 56.SteadyState
94 Minnesota Train 57.SteadyState
95 Minnesota Train 58.SteadyState
96 Minnesota Train 59.SteadyState
97 Minnesota Train 60.SteadyState
98 Minnesota Train 61.SteadyState
99 Minnesota Train 62.SteadyState
100 Minnesota Train 63.SteadyState
101 Minnesota Train 64.SteadyState
102 Minnesota Train 65.SteadyState
103 Minnesota Train 66.SteadyState
104 Minnesota Train 67.SteadyState
105 Minnesota Train 68.SteadyState
106 Minnesota Train 69.SteadyState
107 Minnesota Train 70.SteadyState
108 Minnesota Train 71.SteadyState
109 Minnesota Train 72.SteadyState
110 Minnesota Train 73.SteadyState
111 Minnesota Train 74.SteadyState
112 Minnesota Train 75.SteadyState
113 Minnesota Train 76.SteadyState
114 Minnesota Train 77.SteadyState
115 Minnesota Train 78.SteadyState
116 Minnesota Train 79.SteadyState
117 Minnesota Train 80.SteadyState
118 Minnesota Train 81.SteadyState
119 Minnesota Train 82.SteadyState
120 Minnesota Train 83.SteadyState
121 Minnesota Train 84.SteadyState
122 Minnesota Train 85.SteadyState
123 Minnesota Train 86.SteadyState
124 Minnesota Test 87.SteadyState
125 Minnesota Test 88.SteadyState
126 Minnesota Test 89.SteadyState
127 Minnesota Test 90.SteadyState
128 Minnesota Test 91.SteadyState
129 Minnesota Test 92.SteadyState
130 Minnesota Test 93.SteadyState
131 Minnesota Test 94.SteadyState
132 Minnesota Test 95.SteadyState
133 Minnesota Test 96.SteadyState
134 Minnesota Test 97.SteadyState
135 Minnesota Test 98.SteadyState
136 Minnesota Test 99.SteadyState
137 Minnesota Test 100.SteadyState
138 Minnesota Test 101.SteadyState
139 Minnesota Test 102.SteadyState
140 Minnesota Test 103.SteadyState
141 Minnesota Test 104.SteadyState
142 Minnesota Test 105.SteadyState
143 Minnesota Test 106.SteadyState
144 Minnesota Test 107.SteadyState
145 Minnesota Test 108.SteadyState
146 Minnesota Test 109.SteadyState
147 Minnesota Test 110.SteadyState
148 Minnesota Test 111.SteadyState
149 Minnesota Test 112.SteadyState
150 Minnesota Test 113.SteadyState
151 Minnesota Test 114.SteadyState
152 Minnesota Test 115.SteadyState
153 Minnesota Test 116.SteadyState
154 Minnesota Test 117.SteadyState
155 Minnesota Test 118.SteadyState
156 Minnesota Test 119.SteadyState
157 Minnesota Test 120.SteadyState
158 Minnesota Test 121.SteadyState
159 Minnesota Test 122.SteadyState
160 Minnesota Test 123.SteadyState
161 Minnesota Test 124.SteadyState
162 Minnesota Test 125.SteadyState
163 Minnesota Test 126.SteadyState
164 Minnesota Test 127.SteadyState
165 Minnesota Test 128.SteadyState
166 Minnesota Test 129.SteadyState
167 Minnesota Test 130.SteadyState
168 Minnesota Test 131.SteadyState
169 Minnesota Test 132.SteadyState
170 Minnesota Test 133.SteadyState
171 Minnesota Test 134.SteadyState
172 Minnesota Test 135.SteadyState
173 Paulsboro-Train 136.T=2.2
174 Horsham-Train 137.T=2
175 Warminster-Test 138.T=2
176 Warrington-Train 139.T=2
#Multicheck plot
# Split Steady State Group into different populations for boxplot grouping
#df_check[df_check$Time.desc == "SteadyState" & grepl("Lubeck",df_check$Dataset),]$Time.desc <- "Lubeck"
#df_check[df_check$Time.desc == "SteadyState" & grepl("Little Hocking",df_check$Dataset),]$Time.desc <- "Little Hocking"
Modify aesthetics lookup table for boxplots
## additional source aesthetic lookup table for grey-scale time (years); merged legends save space on plotting output
times <- df_check%>% select(Time.desc, Time) %>% unique () %>%
mutate(rank = rank(Time) , grey = grey.colors(start=1,end=0.4, n = n()),
alpha = (rank)/8) %>%
select(-Time)
df_check <- df_check %>% mutate (legend_label = (paste0(City, "\n", Time.desc ) )) # add legend-labels
aes_lut <- df_check %>%
select(City, Train_Test, datatype,Time, Time.desc, legend_label) %>% unique () %>%
left_join(aes_lut[, c("City", "cols")], by = "City") %>% ungroup () %>% unique ()%>%
left_join (times, by = "Time.desc") %>%
arrange(datatype, City, Train_Test, Time) %>%
mutate(alpha = if_else(City == "Horsham", alpha/2, alpha)) %>% # otherwise too dark with this color
mutate_if(is.factor, as.character)
Changed grey start to 1 instead of 0.8, end at 0.6 instead of 0.4. Changed shape of symbols so they are filled.
#CD
# Decatur
df_decat <- df_check %>%
filter(City == "Decatur" & Train_Test %in% c ("Train", "Test")) %>%
mutate(panel = ordered (Train_Test, levels = c ("Train", "Test"),
labels = c("C: PFOS Decatur Train", "D: PFOS Decatur Test") ))
aes_lut_df_df_decat <- aes_lut %>%
filter(City == "Decatur" & Train_Test %in% c ("Train", "Test")) %>%
mutate_if(is.factor, as.character)
source( paste0(gsub(basename(here()), 'shared_functions', here()), '/plot_sum_boxplot.r'))
plt_train <- plot_sum_boxplot (dframe = df_decat, aes_lut= aes_lut_df_df_decat, facets = TRUE , pfas_nom = pfas_name )
print(plt_train)
ggsave(here ("output-plots",paste0( sa,"DecaturTrainTestboxplot",pfas_name,".pdf")),plt_train,dpi=600)
Saving 6.5 x 3.5 in image
ggsave(here ("output-plots",paste0( sa,"DecaturTrainTestboxplot",pfas_name,".png")),plt_train,dpi=600)
Saving 6.5 x 3.5 in image
Changed grey start to 1 instead of 0.8, end at 0.6 instead of 0.4. Added shapes and fills to data points.
lets <- LETTERS;
names(lets)[1:(length(unique(df_check$dataset))-4)]<-as.character(unique(df_check$dataset))[5:length(unique(df_check$dataset))]
for (d in unique(df_check$dataset)) { # d = unique(df_check$dataset)[11]
ddset <- df_check %>%
filter(dataset == d)
aes_lut_ddset <- ddset %>% select(legend_label, City,Train_Test,datatype, Time.desc ) %>% unique () %>% inner_join(aes_lut)
gt <- ifelse(is.na(lets[d]),d,paste0(lets[d],": ", d))
plt <- plot_sum_boxplot(dframe = ddset, aes_lut= aes_lut_ddset, gtitle= gt, facets = FALSE, pfas_nom = pfas_name)
print(plt)
ggsave(here ("output-plots",
paste0( sa, d,"-boxplot-",
pfas_name,".pdf")) ,
plt,dpi=600)
ggsave(here ("output-plots",
paste0( sa, d,"-boxplot-",
pfas_name,".png")) ,
plt,dpi=600)
}
Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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Joining, by = c("legend_label", "City", "Train_Test", "datatype", "Time.desc")
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### make Training plot
df_d_trt <- df_check %>%
filter( (Train_Test == "Train") & ((Output_Var == "M_Cbgd_Css") | (Output_Var == "M_Cserum"))) %>%
mutate_if(is.factor, as.character) %>% # drop factor levels unused
mutate(Dataset.Time = factor(Dataset.Time))
aes_lut_df_d_trt <- df_d_trt %>% select(City, datatype,Time, Time.desc, legend_label) %>%
inner_join(aes_lut ) %>%
select(-Train_Test) %>% ungroup () %>% unique ()
Joining, by = c("City", "datatype", "Time", "Time.desc", "legend_label")
plt_train <- plot_sum_boxplot(dframe = df_d_trt, aes_lut= aes_lut_df_d_trt,
gtitle="C: Summary Data - Train" , facets = FALSE,
pfas_nom = pfas_name )
print(plt_train)
ggsave(here ("output-plots", paste0( sa, "SummaryTrainDataboxplot",pfas_name,".pdf")), plt_train,dpi=600)
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ggsave(here ("output-plots", paste0( sa, "SummaryTrainDataboxplot",pfas_name,".png")), plt_train,dpi=600)
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### make Test plot
df_d_test <- df_check %>%
filter((Train_Test == "Test") &
((Output_Var == "M_Cbgd_Css") | (Output_Var == "M_Cserum"))) %>%
mutate_if(is.factor, as.character) %>% # drop factor levels unused
mutate(Dataset.Time = factor(Dataset.Time))
aes_lut_df_d_test <- df_d_test %>% select(City, datatype,Time, Time.desc, legend_label) %>%
inner_join(aes_lut ) %>%
select(-Train_Test) %>% ungroup () %>% unique ()
Joining, by = c("City", "datatype", "Time", "Time.desc", "legend_label")
plt_test <- plot_sum_boxplot(dframe = df_d_test, aes_lut= aes_lut_df_d_test,
gtitle="D: Summary Data - Test", facets = FALSE ,
pfas_nom = pfas_name)
print(plt_test)
ggsave(here ("output-plots",paste0( sa, "SummaryTestDataboxplot",pfas_name,".pdf")), plt_test,dpi=600)
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ggsave(here ("output-plots",paste0( sa, "SummaryTestDataboxplot",pfas_name,".png")), plt_test,dpi=600)
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Shows shift in background estimate.
gmscale<-0.8
dat <- multicheck$parms.samp[,grep("M_ln_Cbgd",names(multicheck$parms.samp))]
datasetnames <- as.character(unique(calibdata$dataset))
datasetnames <- gsub(" M","",datasetnames)
datasetnames <- gsub(" F","",datasetnames)
datasetnames<-datasetnames[!duplicated(datasetnames)]
names(dat) <- datasetnames
dat <- dat[,grep("Train",names(dat))]
dat.df <- pivot_longer(dat,1:ncol(dat))
dat.df <- rbind(dat.df,
data.frame(name="Prior",value=rnorm(5000,m=log(gmscale),sd=0.4055)))
dat.df$name <- factor(dat.df$name,levels=rev(
c("Prior",datasetnames[grep("Train",datasetnames)])))
dat.df$value <- exp(dat.df$value)
p<-ggplot(dat.df)+
#geom_violin(aes(x=name,y=value,fill=name=="Prior"))+
geom_boxplot(aes(x=name,y=value,fill=name=="Prior"),outlier.shape=NA)+
scale_y_log10()+coord_flip()+
scale_fill_manual(name=NULL,
values=c("#009988", "#EE7733" )) +
theme_classic() +
geom_hline(yintercept = gmscale,color="grey")+
theme(legend.position="none",
panel.background = element_rect(color="black",size=1))+
ylab("Posterior shift in Background Concentration")
print(p)
ggsave(here ("output-plots",paste0( sa,"PFOS_GM_Cbgd.pdf")) , p, dpi=600)
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ggsave(here ("output-plots",paste0( sa,"PFOS_GM_Cbgd.png")) , p, dpi=600)
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For PFOS, the population GM of the half-life has a posterior distribution that is narrower than the prior, with a posterior median (95% CI) estimate of 3.06 (2.16-4.37) years. The population GSD posterior is larger than the prior at 1.47(1.44-1.75).
dat <- multicheck$parms.samp[,c("M_ln_k.1.","V_ln_k.1.", "M_ln_Vd.1.", "SD_ln_Vd.1.")]
names(dat) <- c("M_ln_k(1)","V_ln_k(1)", "M_ln_Vd(1)", "SD_ln_Vd(1)")
set.seed(3.14159)
dat$z_ln_k <- rnorm(nrow(dat))
dat$z_ln_Vd <- rnorm(nrow(dat))
dat %>% rename_()
dat$ln_k_i <- dat$`M_ln_k(1)` + sqrt(dat$`V_ln_k(1)`)*dat$z_ln_k
dat$ln_Vd_i <- dat$`M_ln_Vd(1)`+ dat$`SD_ln_Vd(1)`*dat$z_ln_Vd
linmod <- lm(ln_Vd_i ~ ln_k_i,data=dat)
ggplot(dat) + geom_point(aes(ln_k_i,ln_Vd_i)) +
labs(subtitle=paste("Adj R2 =",signif(summary(linmod)$adj.r.squared,2)))
qqnorm(dat$ln_k_i,main="ln k Q-Q Normal")
qqline(dat$ln_k_i,col="red")
plot(ecdf(dat$ln_k_i))
x <- seq(-3,1,0.01)
m_ln_k_i <- mean(dat$ln_k_i)
sd_ln_k_i <- sd(dat$ln_k_i)
lines(x,pnorm(x,mean=m_ln_k_i,sd=sd_ln_k_i),col="red")
text(m_ln_k_i-2*sd_ln_k_i,0.9,paste("m =",signif(m_ln_k_i,4),"\nsd =",signif(sd_ln_k_i,4)))
qqnorm(dat$ln_Vd_i,main="ln Vd Q-Q Normal")
qqline(dat$ln_Vd_i,col="red")
plot(ecdf(dat$ln_Vd_i))
x <- seq(-3,1,0.01)
m_ln_Vd_i <- mean(dat$ln_Vd_i)
sd_ln_Vd_i <- sd(dat$ln_Vd_i)
lines(x,pnorm(x,mean=m_ln_Vd_i,sd=sd_ln_Vd_i),col="red")
text(m_ln_Vd_i-2*sd_ln_Vd_i,0.9,paste("m =",signif(m_ln_Vd_i,4),"\nsd =",signif(sd_ln_Vd_i,4)))
hl_i <- log(2)/ exp(dat$ln_k_i) # individual half-life
med_hl_i <- paste(signif (median (hl_i), 3)) # median of individual half-life
ci_med_hl_i <- paste(signif (quantile(hl_i, prob=c(0.025,0.975)), 3),collapse="-") # 95ci med individual halflife
gm_hl_i <- paste(signif (exp(mean(log(hl_i))), 3)) # gm (which should be really close)
gsd_hl_i <- paste(signif (exp(sd(log(hl_i))), 3)) # gsd individual
med_Vd_i <- paste(signif (median(exp(dat$ln_Vd_i)), 3)) # median individual Vd
ci_med_Vd_i <-paste(signif (quantile(exp(dat$ln_Vd_i), prob=c(0.025,0.975)), 3),collapse="-") # 95ci med individual Vd
gm_vd_i <- paste(signif (exp(mean(dat$ln_Vd_i)), 3)) # gm (which should be really close)
gsd_vd_i<- paste(signif (exp(sd(dat$ln_Vd_i)), 3)) # gsd indiv
med_CL_i <- paste(signif (median(exp(dat$ln_Vd_i+dat$ln_k_i)), 3)) # median individual CL
ci_med_CL_i <-paste(signif (quantile(exp(dat$ln_Vd_i+dat$ln_k_i), prob=c(0.025,0.975)), 3),collapse="-") # 95ci med individual CL
ci98_med_CL_i <-paste(signif (quantile(exp(dat$ln_Vd_i+dat$ln_k_i), prob=c(0.01,0.99)), 3),collapse="-") # 98ci med individual CL
gm_CL_i <- paste(signif (exp(mean(dat$ln_Vd_i+dat$ln_k_i)), 3)) # gm (which should be really close)
gsd_CL_i<- paste(signif (exp(sd(dat$ln_Vd_i+dat$ln_k_i)), 3)) # gsd indiv
PFOS_priors <- data.frame(
halflife_GM= log(2)/rlnorm(50000,
meanlog=-1.8971,sdlog=0.4055))
M_k <- exp(as.numeric(dat$`M_ln_k(1)`))
PFOS_halflife_GM <- log(2)/M_k
PFOS_hlgm_pr_med <- signif(median(PFOS_priors$halflife_GM,3))
PFOS_hlgm_pr_med_95ci <-paste(signif(quantile(PFOS_priors$halflife_GM,
prob=c(0.025,0.975)),
3),
collapse="-")
PFOS_hl_median_gm <- signif(median(PFOS_halflife_GM),3)
PFOS_hl_median_gm_95ci <- paste(signif(quantile(PFOS_halflife_GM,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(halflife_GM, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_halflife_GM,stat(density),color="Posterior"),geom="line",size=1.5 )+
xlim(0,15)+
labs(title = bquote("C: PFOS"~T[1/2]~"Population GM") ,
subtitle=paste("Posterior Median (95% CI): ",
PFOS_hl_median_gm," (",
PFOS_hl_median_gm_95ci,
")",sep=""))+
xlab(bquote("Population GM"~T[1/2]~"(yrs)")) +
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
Warning: Removed 80 rows containing non-finite values (stat_density).
ggsave(here ("output-plots",paste0( sa,"PFOS_hl_gm.pdf")), p, dpi=600)
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Warning: Removed 80 rows containing non-finite values (stat_density).
ggsave(here ("output-plots",paste0( sa,"PFOS_hl_gm.png")), p, dpi=600)
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Warning: Removed 80 rows containing non-finite values (stat_density).
PFOS_priors$halflife_GSD = exp(sqrt(exp(rnorm(50000,m=log(0.1987),sd=log(1.267)))))
PFOS_halflife_GSD <- exp(sqrt(dat$`V_ln_k(1)`))
PFOS_hlgsd_pr_med <- signif(median(PFOS_priors$halflife_GSD,3))
PFOS_hlgsd_pr_med_95ci <-paste(signif(quantile(PFOS_priors$halflife_GSD,
prob=c(0.025,0.975)),
3),
collapse="-")
PFOS_hl_gsd_med <- signif(median(PFOS_halflife_GSD),3)
PFOS_hl_gsd_med_95ci <- paste(signif(quantile(PFOS_halflife_GSD,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(halflife_GSD, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_halflife_GSD,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(1,3)+
labs(title = bquote("D: PFOS"~T[1/2]~"Population GSD"),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_hl_gsd_med," (",
PFOS_hl_gsd_med_95ci,
")",sep=""))+
xlab(bquote("Population GSD"~T[1/2]))+
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" ))+
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_hl_gsd.pdf")), p, dpi=600)
ggsave(here ("output-plots",paste0( sa, "PFOS_hl_gsd.png")), p, dpi=600)
For PFOS, the data were not particularly informative, but slightly increased the estimate of the median to 0.308(0.223-0.548) slightly. They were not informative as to the population GSD, with the posterior distributions essentially unchanged from the priors.
PFOS_priors$Vd_GM <- rlnorm(50000,
meanlog=-1.46968,
sdlog=0.2624)
PFOS_Vd_GM <- exp(dat$`M_ln_Vd(1)`)
PFOS_vd_gm_pr_med <- signif(median(PFOS_priors$Vd_GM,3))
PFOS_vd_gm_pr_med_95ci <- paste(signif(quantile(PFOS_priors$Vd_GM,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_vd_gm_med <- signif(median(PFOS_Vd_GM),3)
PFOS_vd_gm_med_95ci <- paste(signif(quantile(PFOS_Vd_GM,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(Vd_GM, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_Vd_GM,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(0,1)+labs(title = bquote("C: PFOS"~V[d]~"Population GM"),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_vd_gm_med," (",
PFOS_vd_gm_med_95ci,")",sep=""))+
xlab(bquote("Population GM"~V[d]~"(l/kg)"))+
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" )) + theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_vd_gm.pdf")), p, dpi=600)
ggsave(here ("output-plots",paste0( sa, "PFOS_vd_gm.png")), p, dpi=600)
PFOS_priors$Vd_GSD = exp(abs(rnorm(50000,sd=0.17)))
PFOS_Vd_GSD <- exp(dat$`SD_ln_Vd(1)`)
PFOS_vd_gsd_pr_med <- signif(median(PFOS_priors$Vd_GSD,3))
PFOS_vd_gsd_pr_med_95ci <- paste(signif(quantile(PFOS_priors$Vd_GSD,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_vd_gsd_med <- signif(median(PFOS_Vd_GSD),3)
PFOS_vd_gsd_med_95ci <- paste(signif(quantile(PFOS_Vd_GSD,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(Vd_GSD, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_Vd_GSD,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(1,3)+
labs(title = bquote("D: PFOS"~V[d]~"Population GSD "),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_vd_gsd_med," (",
PFOS_vd_gsd_med_95ci,
")",sep=""))+
xlab(bquote("Population GSD"~V[d]))+
scale_color_manual(name=NULL,
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_vd_gsd.pdf")), p, dpi=600)
ggsave(here ("output-plots",paste0( sa, "PFOS_vd_gsd.png")), p, dpi=600)
Cl is k * Vd
PFOS_priors$CL_GM <- PFOS_priors$Vd_GM * (log(2)/PFOS_priors$halflife_GM)
PFOS_CL_GM <- exp(dat$`M_ln_Vd(1)` + dat$`M_ln_k(1)`)
PFOS_cl_gm_pr_med <- signif(median(PFOS_priors$CL_GM,3))
PFOS_cl_gm_pr_med_95ci <- paste(signif(quantile(PFOS_priors$CL_GM,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_cl_gm_med <- signif(median(PFOS_CL_GM),3)
PFOS_cl_gm_med_95ci <- paste(signif(quantile(PFOS_CL_GM,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(CL_GM, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_CL_GM,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(0,0.25)+labs(title = "B: PFOS Clearance Pop. GM ",subtitle=paste("Posterior Median (95% CI): ",
PFOS_cl_gm_med," (",
PFOS_cl_gm_med_95ci,
")",sep=""))+
xlab("Pop. GM CL (l/(kg-yr))")+
scale_color_manual(name=NULL,#
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa, "PFOS_CL_gm.pdf")), p, dpi=600)
ggsave(here ("output-plots",paste0( sa, "PFOS_CL_gm.png")), p, dpi=600)
PFOS_priors$CL_GSD = exp(sqrt(log(PFOS_priors$Vd_GSD)^2 +
log(PFOS_priors$halflife_GSD)^2))
PFOS_CL_GSD <- exp(sqrt(log(PFOS_Vd_GSD)^2 +
log(PFOS_halflife_GSD)^2))
PFOS_CL_gsd_pr_med <- signif(median(PFOS_priors$CL_GSD,3))
PFOS_CL_gsd_pr_med_95ci <- paste(signif(quantile(PFOS_priors$CL_GSD,
prob=c(0.025,0.975)), 3), collapse="-")
PFOS_CL_gsd_med <- signif(median(PFOS_CL_GSD),3)
PFOS_CL_gsd_med_95ci <- paste(signif(quantile(PFOS_CL_GSD,
prob=c(0.025,0.975)),3),collapse="-")
p<-ggplot()+
stat_density(aes(CL_GSD, color = "Prior"),data=PFOS_priors,geom="line",size=2)+
stat_density(aes(PFOS_CL_GSD,stat(density), color = "Posterior"),geom="line",size=1.5)+
xlim(1,3)+
labs(title = bquote("H: PFOS"~CL~"Population GSD "),
subtitle=paste("Posterior Median (95% CI): ",
PFOS_CL_gsd_med," (",
PFOS_CL_gsd_med_95ci,
")",sep=""))+
xlab(bquote("Population GSD"~CL))+
scale_color_manual(name=NULL,
values=c(Prior="#009988", Posterior="#EE7733" )) +
theme_classic() +
theme(legend.title = element_blank(),legend.position=c(0.8,0.7),
panel.background = element_rect(color="black",size=1),
legend.background = element_rect(fill="transparent", color=NA))
print(p)
ggsave(here ("output-plots",paste0( sa,"PFOS_CL_gsd.pdf")) ,p,dpi=600)
ggsave(here ("output-plots",paste0( sa,"PFOS_CL_gsd.png")) ,p,dpi=600)
PFOS_hlgm_pr_med <- paste(signif(PFOS_hlgm_pr_med, 3))
PFOS_hl_median_gm<- paste(signif(PFOS_hl_median_gm, 3))
PFOS_hlgsd_pr_med<- paste(signif(PFOS_hlgsd_pr_med, 3))
PFOS_hl_gsd_med<- paste(signif(PFOS_hl_gsd_med, 3))
PFOS_vd_gm_pr_med<- paste(signif(PFOS_vd_gm_pr_med, 3))
PFOS_vd_gm_med<- paste(signif(PFOS_vd_gm_med, 3))
PFOS_vd_gsd_pr_med<- paste(signif(PFOS_vd_gsd_pr_med, 3))
PFOS_vd_gsd_med<- paste(signif(PFOS_vd_gsd_med, 3))
PFOS_cl_gm_pr_med<- paste(signif(PFOS_cl_gm_pr_med, 3))
PFOS_cl_gm_med<- paste(signif(PFOS_cl_gm_med, 3))
| Parameter | Prior GM | Posterior GM | Prior GSD | Posterior GSD |
|---|---|---|---|---|
| Half-life (years) | 4.62 | 3.42 | 1.56 | 1.57 |
| HL [95% CI] | [2.08-10.3] | [2.62-4.5] | [1.42-1.76] | [1.43-1.75] |
| Volume of distribution | 0.23 | 0.322 | 1.12 | 1.11 |
| \(V_D\) [95% CI] | [0.137-0.384] | [0.221-0.47] | [1.01-1.46] | [1-1.41] |
| Clearance | 0.0344 | 0.0656 | ||
| \(CL\) [95% CI] | [0.0133-0.0894] | [0.0478-0.0923] | [] | [] |
| Parameter | median GM [95% CI] | GM calculator input | GSD individual |
|---|---|---|---|
| Half-life (years) | 3.35 [ 1.25-8.61 ] | 3.36 | 1.63 |
| Volume of distribution \(V_D\) | 0.322 [ 0.184-0.547 ] | 0.321 | 1.3 |
| Clearance (L/kg-yr) | 0.067 [ 0.0245-0.176 ] [[ 0.0203-0.199 ]] | 0.0664 | 1.65 |
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.3 (2020-02-29)
os Red Hat Enterprise Linux Server 7.9 (Maipo)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2022-01-23
─ Packages ───────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [2] CRAN (R 3.6.3)
backports 1.2.1 2020-12-09 [2] CRAN (R 3.6.3)
bayesplot * 1.8.0 2021-01-10 [2] CRAN (R 3.6.3)
broom 0.7.5 2021-02-19 [2] CRAN (R 3.6.3)
bslib 0.2.4 2021-01-25 [2] CRAN (R 3.6.3)
cachem 1.0.4 2021-02-13 [2] CRAN (R 3.6.3)
callr 3.5.1 2020-10-13 [2] CRAN (R 3.6.3)
cellranger 1.1.0 2016-07-27 [2] CRAN (R 3.6.3)
cli 2.3.1 2021-02-23 [2] CRAN (R 3.6.3)
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[1] /home/ad.abt.local/layc/R/x86_64-pc-linux-gnu-library/3.6
[2] /opt/R/3.6.3/lib64/R/library